SALib is a Python library for global sensitivity analysis (GSA) of computational models. It implements a comprehensive suite of variance-based and moment-independent methods including Sobol', Morris, FAST, DGSM, PAWN, HDMR, RBD-FAST, and fractional factorial designs. The library follows a decoupled workflow: sample parameter space, evaluate the model, then analyze results. It provides both a programmatic API (import SALib) and a command-line interface (salib sample/analyze). The architecture is modular: sampling modules in SALib.sample, analysis modules in SALib.analyze, with shared utilities in SALib.util. A high-level ProblemSpec class enables fluent method chaining (sample → evaluate → analyze → plot). Results are returned as ResultDict objects with to_df() and plot() methods for integration with pandas and matplotlib. External dependencies are numpy, scipy, matplotlib, pandas, and optionally multiprocess/pathos for parallel execution. The library is used in environmental modeling, energy systems, and uncertainty quantification research, with over 1000 GitHub stars and a JOSS paper.